{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 4, 5, 6]\n",
    "y = [3, 7, 14, 19, 15, 11]\n",
    "plt.plot(x, y)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(0,10,20)\n",
    "x=np.sin(x)*10\n",
    "y=np.random.randn(20)*10\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          5.02351155  8.68729618  9.9996678   8.60540338  4.88189209\n",
      " -0.16301361 -5.163796   -8.76688031 -9.99701037 -8.52122368 -4.73897526\n",
      "  0.3259839   5.30270815  8.84413462  9.99169621  8.43477945  4.59479904\n",
      " -0.48886756 -5.44021111]\n"
     ]
    }
   ],
   "source": [
    "from math import sin\n",
    "x=np.linspace(0,10,20)\n",
    "x=np.sin(x)*10\n",
    "y=np.random.randn(20)*10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(y,\"^--g\",label=\"Y\")\n",
    "plt.plot(x,'o-b',label=\"X\")\n",
    "plt.title(\"test matplotlib\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xticks(np.arange(0,20,2))\n",
    "plt.yticks(np.arange(-25,25,5))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMIN\\AppData\\Local\\Temp/ipykernel_32372/4184940355.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  figure.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "figure = plt.figure()\n",
    "axes1 = figure.add_subplot(1,2,1)\n",
    "axes2 = figure.add_subplot(1,2,2)\n",
    "axes1.plot([1,3,5,7],[4,9,6,8],label=\"Line A\")\n",
    "axes2.plot([1,2,4,5],[8,4,6,2],label=\"Line B\")\n",
    "axes1.set(xlim=[0,8],ylim=[0,10],title='an example axes',ylabel='Y-axis',xlabel='X-axis')\n",
    "axes2.set(xlim=[0,6],ylim=[0,10],title='an example axes',ylabel='Y-axis',xlabel='X-axis')\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f1494fdeb0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nbulpdCm2kZOWjIcH5WDWj3uweidvhhFuwU6EJsQmRMUFRMGYMGBCUGvTzTJBGpWBvnL7EczbuA+PXdMRWS2D232NfPuPwZ2QkZKIZ+ZshqvG+F9B7YoToeERygTpvOJ5Yaqq8aIu0F01Cs/P24KMlEQ8mtfR6HJsJykuFhNv6o6i/Sfx6ZpSo8uxJU6Ehk8oE6QV1RWGT442KtBFZKiIFItIiYg87eP5a0TkhIisd389o3+p+pieX4rCfeV4elg3ToSGyU29WqN/dipe+aoY5Sa+ZZ0V2zUnQsMvlAlSoydHGwx0EXEAeBvAMAA9ANwlIj18vPRbpVRv99fzOtepi/KzVXh5YTH6ZbfALZe0Mboc2xIRPHNrDxytqMRbS0uMLscnq7ZrToRGRrATpEZPjjam2v4ASpRSO5RSlQCmAQhuAwSDvb20BEcrKvHMLT35K2eYXZzRHGP6tsP/fr8TOw8Hfgu0CLBku+ZEaOQEM0Fq9ORoYwI9A4D3YGiZ+3t1DRCRDSLyLxHxuXRERMaLSL6I5B86dCiIcoO3+0gF/vf7n3DbZe3Qq13ziH52tPrNjV0R54jBXxcUGl2KL7q1ayAybZsToZHlmSANhNFXjzYm0H11ZetWuw5Ae6XUpQDeBDDb1xsppaYopXKVUrnp6ekBFRqqlxYWwREjeOrGrhH93Gh2UdME/OKajvhqywEzLmPUrV0D4W/bnAiNPM8EaaCMvHq0MYFeBsB7C8J2APZ6v0ApVa6UOuU+XwDAKSJpulUZoh93H8O8jfvw8KAOaMWtcSPqwas6oHWzBLywoNAUV9J5sUy75kSocZwOZ1A3ljbq6tHGBPoaAJ1FJEdE4gCMBXBeyxKR1uL+619E+rvf94jexQZDKYUXFxQirUk8HhnUwehyok5inAMTbuiCDaXHzXYjDMu0a06EGuvmzjdb5urRBqtUSlUDeBzAQgCFAKYrpQpE5FERedT9stsBbBaRDQAmAxirTNIdW1hwAGt+OoZfX98FyfGxRpcTlUZf1g7d2zTDf31ZhHPVLqPLAWCtds2JUGNZ6epRMSp3c3NzVX5+flg/o8pVgxtfW46YGMGXT16NWEfUXUdlGt9uO4R731uNP97cHQ9dHf7flERkrVIqN+wf5IPebTv5xeSAe+eJsYkY0W0EPh79McfOQ6SUwt0z78acojkB/8Wa4EhAxR8qdP1vUF/btnXCTc8vxY7Dp/G7od0Y5ga7unM6ru6chre+LjH1xUZmU+WqCuouRJwI1Y+Vtte1bcqdqXThjcXbkNu+Ba7rfpHR5RCA3w3thuMVVZjyzQ6jS7EEz2RooDgRqj+rbK9r20B///udOHjyHJ4e1o29FJO4OKM5br20Ld77bif3TG8Ez2RoIDgRGj5W2F7XloF+7HQl/r5sO67rfhFys1ONLoe8TLi+C6pcNXhjyTajSzG9YCZDOREaXmafILVloP/3shKcqqzGUzd2M7oUqiM7LRl39c/CtDWlZt0SwBSCvSqUV4SGl9nvP2q7QN9/4iw+XLkLo/pkoGtr3vTZjJ64thPiHDF4bdFWo0sxpVCuCuVEaHiZfYLUdoH+5tJtUErh/13XxehSyI+Lmibg/oHZ+GLjXhTuKze6HFMJ5arQZGcyJ0IjwMwTpLYK9F1HTuPTNaUY2y8Lmam8E5GZPTKoA5rEx+KVr9hL9xbKVaE3d7k5TFVRXWadILVVoL++eBtiHYInhnQyuhRqQEpSHB4Z1AGLCw9g3e5jRpdjGrwq1DrMOEFqm0Av3n8Ss9fvwX0DsnERN+CyhAcG5qBlchxeXlhsdCmmMX/bfG6PaxGhTJDO3zo/LDXZJtBfW7QVyXGxvE+ohSTHx+KxwZ2wYvsRrNh+2OhyDKeUwpmqwHvnvCrUGCHdf7SqIizj6LYI9M17TuDLgv34+VU5aJEcZ3Q5FIB7Ls9Cq2bxePWrrWbbXjeiPCtb1AVbstePV4UaK9gJUgUVlhUvtgj01xZtRbOEWDx4VY7RpVCAEpwOPD64E/J3HcPybdHZSw92ZQuvCjWHYCdIw7HixfKBvr70OJYUHcT4QR3QPJG9FCu6o18mMlIS8eqi6OylB7uyhROh5hHs/Uf1XvFi+UB/ddFWtEhy4v6B7J1bVXysA08M6YQNpcextOig0eVEXDArWzgRai7BTpDqveLF0oGe/9NRLN96CI/mdUQT3rzC0m7r2w5ZqUlR2UsPZmULJ0LNJdgJUr1XvFg60F9fvA1pTeIwbkC20aVQiJyOGPzy2s4o2FuORVsOGF1OxASzskUgnAg1Ic8Eqfi8/7h/eq54sWygr/npKL4rOYxH8zoiMc5hdDmkg5G92yK7ZRJeX7wtKnrpwa5sCeamxRQZIoJEZ2DDLnqueLFsoL+2aCvSmsTjnsvbG10K6STWEYMnhnTGln3lWFhg7156KCtbeIm/uQVzU2m9VrxYMtBX7TiCFduP4NG8Duyd28yI3m2Rk5aMN5ZsQ02NfXvpXNliX0aueLFkoL++eBvSm8bjZ1ewd243sY4Y/PLaTijcV46vtuw3upyw4coW+zJyxYvlAn3VjiNYueMIHs3riAQne+d2dOslbdEhLRmvL7ZnLz3Ym1dwZYs1hLLiZV7xvJA+23KB/sYSrXd+z+VZRpdCYRLriMET13ZC0f6T+MpmK16CvXkFV7ZYS9ArXqorQpoctVSgr/npKFZsP4JHBnVg79zmbr1EG0ufvMQ+K15CuXkFV7ZYTzArXgCENDlqqUB/Y/E2rmyJErGOGDw+uBO27LPPunTevCL6BLPiJZTJUcsE+tpd2rrzRwZxZUu0GOFel/6GTXrpvHlF9Al2xUuwk6OWCfTXF29Dy+Q43HMFx86jRawjBv8xuBMK9pZjSaH193jhzSuij2fFSyBC2Q7AEoG+bvcxfLvtMMYP6oCkOO7ZEk1G9clAVmoSJi+1fi+dN6+IPp4VL4EKdjsASwT6m0u2ITU5juvOo5BnLH1j2Qks23rI6HKCVuWqCngslTevsAenwxnwpHaw2wGYPtA3lh3H18WH8NDVOUjmjopRadRlGchIScQbFt3jxbO6JZA9W3jzCnuJ1HYApg/0yUtKkJLk5I6KUczpHktfX3oc35VY765GntUtgYyfcyLUXiK1HYCpA33znhNYXHgADw7M4X7nUe62vhlo2zzBkr30QFe3OMTBiVCbidR2AKYO9LeWlqBpQizuG5htdClksPhYB35xTUfk7zqGlduPGF1OQIJZ3cKJUHuJ1A0wTBvoRfvL8WXBfjwwMAfNEjgpRMCY3Ey0ahaPyUu3GV1KowVzAwsFxYlQG4rEDTBMG+hvLi1Bk/hYPMh7hZJbgtOBRwZ1xA87jmL1zqNGl9OgYG9gEeiv5WQd4b4BhikDveTgSSzYtA/3XdkezZPYU6Fad/XPQlqTeLxp8l46b2BB/oRzxUuj3lVEhopIsYiUiMjTPp4XEZnsfn6jiFwWULV1vLW0BIlOBx68qkMob0M2lBjnwPhBOfh222Gs230spPcKZ7vmDSzIn3CueGkw0EXEAeBtAMMA9ABwl4j0qPOyYQA6u7/GA3gnoGq97Dx8GnM37MXPrmiP1OS4YN+GbOyey9ujRZITby4Jvpce7nbNG1iQP+Fc8dKYHnp/ACVKqR1KqUoA0wDUnaodAWCq0vwAIEVE2gRUrdvbX5fA6YjBw1ezd06+JcfH4qGrO+Dr4kPYWHY82LcJa7sOZmULL/OPDuFc8dKYxd0ZAEq9HpcBuLwRr8kAsM/7RSIyHlpPB1lZvjfZurNfJi7LaoH0pvGNKI2i1bgB7XGuugbtWgS9T7hu7Rq4sG0HurLFcwMLig6eFS+fbv40oEnzhn7ra0wP3Vd3oW4FjXkNlFJTlFK5Sqnc9PR0nx/WLzsVd/NuRNSApglO/Pr6LqEMy+nWroEL23agKxl4A4voE8yKl4aGaRoT6GUAMr0etwOwN4jXEJlJWNt1ICsZuLIleundThrzTmsAdBaRHBGJAzAWQN21WHMBjHOvCrgCwAml1AW/lhKZSFjbdSArGbiyJXrp3U6kMVcgichNAF4H4ADwvlLqBRF5FACUUn8XbRbnLQBDAVQAeEApld/Aex4CsMvP02kArLQLE+sNr2Dqba+U8j2u5xaOdu1+X61tpyIH8UhB/R2nGpzDcRzFzsb8oQwQDW3FSGlIRdMA24nftt2oQI80EclXSuUaXUdjsd7wslq99bHan4X1hpfe9ZrySlEiIgocA52IyCbMGuhTjC4gQKw3vKxWb32s9mdhveGla72mHEMnIqLAmbWHTkREAWKgExHZhOkCvaEtTc1ERN4XkYMistnoWhpDRDJF5GsRKRSRAhF50uia6iMiCSKyWkQ2uOt9zuiagmWldg1Yq22zXXu9t5nG0N1bmm4FcD20y67XALhLKbXF0ML8EJFBAE5B25HvYqPraYh7p8A2Sql1ItIUwFoAI03871cAJCulTomIE8B3AJ5073xoGVZr14C12jbbdS2z9dAbs6WpaSillgMw/73Q3JRS+5RS69znJwEUQts90JTc29aecj90ur/M0wNpPEu1a8BabZvtupbZAt3fdqWkMxHJBtAHwCqDS6mXiDhEZD2AgwAWKaVMXa8fbNcREu3t2myB3ujtSil4ItIEwOcAfqWUKje6nvoopVxKqd7QdjrsLyKm/vXfD7brCGC7Nl+gcxveMHOP2X0O4COl1Eyj62kspdRxAMugbZRlNWzXYcZ2rTFboDdmS1MKknsy5j0AhUqpV42upyEiki4iKe7zRADXASgytKjgsF2HEdt1LVMFulKqGsDjABZCm9iYrpQqMLYq/0TkEwArAXQVkTIRedDomhowEMC9AIaIyHr3101GF1WPNgC+FpGN0EJxkVJqnsE1Bcxq7RqwXNtmu3Yz1bJFIiIKnql66EREFDwGOhGRTTDQiYhsItboAojsJi0tTWVnZxtdBtnU2rVrD/u7pygDnagBIvI+gFsAHGzMvibZ2dnIz/dzL+lJk7QvoiCJyC5/z3HIhahhH0CvC5qe89pYzxPs3gHPsKcQMNCJGqDbRlVHjmjHb78F8vO1cK+sPD/kfQU+USMx0Il0ICLjRSRfRPIPHTp0/pOTJgEiQFqa9njQIKBfP+08Pr72dcXF2nHdOqC6muFOAWOgE+lAKTVFKZWrlMpNT68zXzVpEqAUcOyY/zcQAbp108779gWcTu188mSgvJzhTo3CQCeKlJQU7aiU9uU59xxrai78mSefBJo3187z84GffqoNdwY71cFAJ4qkZ5/1/5y4d9n1Dnxv/foBOTna+cyZ7LXTBRjoRA3QdaMq7+D1hLt3yNcNfH/hfttt2nHUKO34nGVvt0o6YqATNUApdZdSqo1SyqmUaqeUek+XN25o2aKvcK/7vdmza3v2x49f+B4UVRjoRGblK9w93/PVa2/RQgt3DsVELQY6kRX467l7D8nc5LUF+Mcfa9/nUExUYaATWY0n3OsOvyxYUHt+zz1ATJ3/vdlbtz0GOpFV1e21+5tAFblwKIZsiYFOZAd1e9/e4f7ww7XLHf29nmyBuy0S2U3doZh//KP23LMiBmCo2xB76ER242so5vTp2u916eL7tWR57KET2ZknsJOSar+3dat2ZG/ddthDJ4oWnt66y6U99uwd88QT2pGhbnkMdKJo4Qns55/Xjp5ljWlpXAVjEwx0omgzaVJtbx0AsrKAuDhDSyJ9MNCJopH38Mru3dqdk4DaNevXXGNEVRQiBjpRNPP01KurtcdLl2qPv/nG2LooKAx0omjm6ak7HNpx8ODa57ZsOf81ZHpctkhEmry885cy9uxZe85QtwT20IlIs2zZ+VsG9OpV23MnS2CgE5FvmzbVrlnnZKklMNCJ6EKeyVLP6pd33uFkqQUw0InoQp4xc6dTOz76aO1zZ86c/xoyDU6KElH96k6Weu8Lw1A3FfbQiah+dSdLnU7g0ksNLYl8Y6ATUWCqqoANG7Rzz2Qpe+qmwEAnosbzTJZ+/732+He/044MdFMQ5esehEQUtNzcXJWfn290GeEnom0ZEBurbcXrPc5OYSMia5VSub6eYw+diALn6ZHHutdVxMRwnboJMNCJKHCTJp0/UZqVBaSkcJ26wRjoRBS65cuB1FTtfMUK7chx9YjjOnQiCk1eHpCdXft44MDac4Z6RLGHTkShqbtO/c47gSNHDC0pWjHQiUhfn34KtGypnXNTr4hioBORfjzr1D299aQkrQfPydKIYKATkX7qjpm3bw/cdJMhpUQjBjoRhUdeHlBYCFRUaI85/BJ2XOVCROGxbJl23L8faNNGW6u+bRsQH29oWXbGHjoRhVfr1tpx7lwgLs7YWmyOgU5E4ZeXB/TuXbvfC3dpDAsGOlEDRGSoiBSLSImIPG10PZbkWat+4oT2OCVFOzLQdcVAJ6qHiDgAvA1gGIAeAO4SkR7GVmVhzZppx+bNteP69YaVYkcMdKL69QdQopTaoZSqBDANwAiDa7IuT4981y7t2KcPV77oiIFOVL8MAKVej8vc3zuPiIwXkXwRyT906FDEirOcurs0ZmYCH33EC490wkAnqp+vuzZccFcYpdQUpVSuUio3PT09AmXZRFERcPfd2nllpbG12AADnah+ZQAyvR63A7DXoFrsJS8PSE6uXfkSH8/hlxDxwiKi+q0B0FlEcgDsATAWwN3GlmQTnguPAC3I09K0UOfwS9DYQyeqh1KqGsDjABYCKAQwXSlVYGxVNrV4MXDmjHa+c6extVgUe+hEDVBKLQCwwOg6bM1z4ZFHhw613/fuyVO92EMnIuPVvUnGhAmAy8XhlwAx0InIfF5+GYhxx1Ope9UoryptEIdciMhc8vJqV74A2i6NHgz1erGHTkTmUnf4pVmz2jF1qhcDnYjMrbwc2LFDO+dNMurFQCci8/Lco3TVKu3xmDHaY06W+iRKXXAVMxGFIDc3V+Xn5xtdhv2IAMeOaVvvitQOyUQZEVmrlMr19Rx76ERkDXl5QIsWvElGPRjoRGQNnsnSrVu1x61aaUcG+r8x0InIWjp31o6ederr1hlXi8kw0InIWjw98n37tGPfvlz54sZAJyJrqXuTjCuvBDZv5soXMNCJyOq++w7o2VM7//FH7Ril4+q89J+IrCsvr3YsHQAuu6z2PApDnT10IrKuutsEDBtW+1wUrlNnoBORffzrX7XnMTFRN1nKQCcie/BsE+ByaY8dDmDlyqiaLGWgE5E9eMbMPWPqGzcCV1yhnVdWnv8am+KkKBHZT15e7coXQLv5tIeNQ509dCKyn7qTpa1bA82bn/8aGwY7e+hEZH/799eee98NyWahzh46EdmbZ7L05Mna7335pXH1hBEDnYjszdMLb9Kk9ntDh2pHm23By0Anoujh6a17xtbbt9eODHQiIoupG9yeidKJE4GqKssHOwOdiKKPJ7g3btSOf/0rEBcHPPecYSXpgYFORNGn7ha806YBzZpd+BqLYaATEY0dC5SXa+eeiVLv3rpFwp2BTkTRre5EqcsF9Oqlnf/zn9r3LTIUw0AnouhWt/ftcACbNmnn48adv9+6r9ebCAOdiMijbm/dm6+hGJNhoBMRedTtfSsF7NmjnVdX1wZ9dbUpe+oMdCIiX559Vju2basdY2Nr94FxOrWeuuc1Jgl3BjoRkS/eIe0Ziqmp0R5366YdP/8c+L//M82KGAY6EVFDPCHtCe6iIu1YUADce2/t6+quiIlwuDPQiYgaa9Kk2t56XSK1K2Lmz9e2EvAX7mEKegY6kR8iMkZECkSkRkRyja6HTMI7jP2tiLnlFm0rAQBYvFg7eoe759xfyAcZ+Ax0Iv82AxgNYLnRhZAJeSZEPfyF+/XX106m/va3wMyZ2rnL5Tvk654HgHcsIvJDKVUIAOJ9hxsij7qTpt48wV637fztb7Xnse74vece4KOPtPPnngMSEoIuiT10Ih2IyHgRyReR/EOHDhldDkVaQ+HuCfizZ4EHHzz/+Y8/rg3+SZOAp5/WzoO4+YYoX78iEEUJEVkMoLWPp/6glJrjfs0yAL9RSuU35j1zc3NVfn6jXkp2N2lSbSCLnN9zV+rC77lcWugnJ/sevgEgImuVUj7ndDjkQlFNKXWd0TWQjfnrudftxXvExABJSUF/HIdciIgiwdcqFn8h7y/wG8AhFyI/RGQUgDcBpAM4DmC9UurGhn6OQy4UTvUNuTDQiXQmIocA7PLzdBqAwxEsJ1SsN7yCqbe9Uird1xMMdKIIEpF8f70rM2K94aV3vRxDJyKyCQY6EZFNMNCJImuK0QUEiPWGl671cgydiMgm2EMnIrIJBjoRkU0w0IkiQESGikixiJSIyNNG19MQEXlfRA6KyGaja2mIiGSKyNciUujev/5Jo2uqj4gkiMhqEdngrje4vXJ9vTfH0InCS0QcALYCuB5AGYA1AO5SSm0xtLB6iMggAKcATFVKXWx0PfURkTYA2iil1olIUwBrAYw0679f0fZjTlZKnRIRJ4DvADyplPoh1PdmD50o/PoDKFFK7VBKVQKYBmCEwTXVSym1HMBRo+toDKXUPqXUOvf5SQCFADKMrco/pTnlfuh0f+nSs2agE4VfBoBSr8dlMHHgWJmIZAPoA2CVwaXUS0QcIrIewEEAi5RSutTLQCcKP1+3POJYp85EpAmAzwH8SilVbnQ99VFKuZRSvQG0A9BfRHQZ1mKgE4VfGYBMr8ftAOw1qBZbco9Ffw7gI6XUTKPraSyl1HEAywAM1eP9GOhE4bcGQGcRyRGROABjAcw1uCbbcE8yvgegUCn1qtH1NERE0kUkxX2eCOA6AEV6vDcDnSjMlFLVAB4HsBDahN10pVSBsVXVT0Q+AbASQFcRKRORBxv6GQMNBHAvgCEist79dZPRRdWjDYCvRWQjtL/sFyml5unxxly2SERkE+yhExHZBAOdiMgmGOhERDbBQCcisgkGOhGRTTDQiYhsgoFORGQT/x+F5Pvfgv2LyAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0,np.pi)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(224)\n",
    "ax1.plot(x,y_sin)\n",
    "ax2.plot(x,y_sin,'go--',linewidth=2,markersize=12)\n",
    "ax3.plot(x,y_cos,color='red',marker='+',linestyle='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 太阳花\n",
    "import turtle\n",
    "import time\n",
    "turtle.color(\"red\",\"yellow\")\n",
    "turtle.begin_fill()\n",
    "for _ in range(50):\n",
    "    turtle.forward(200)\n",
    "    turtle.left(170)\n",
    "    turtle.end_fill()\n",
    "\n",
    "turtle.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyvista'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12748/732144037.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpyvista\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSphere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mradius\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcenter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pyvista'"
     ]
    }
   ],
   "source": [
    "import pyvista as pv\n",
    "import numpy as np\n",
    "import time\n",
    "start_time=time.time()\n",
    "data = [pv.Sphere(radius=0.01,center=(np.random.random(),np.random.random(),np.random.random())) for i in range(1000)]\n",
    "blocks = pv.MultiBlock(data)\n",
    "blocks.plot(jupyter_backend='static',show_axes=1)\n",
    "end_time=time.time()\n",
    "print('the time cost of ploting {}spheres is: {}s'.format(len(data),end_time-start_time))"
   ]
  },
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   "source": []
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